Extreme events and gender-based violence: a mixed-methods systematic review
Bibliographic record
Abstract
The intensity and frequency of extreme weather and climate events are expected to increase due to anthropogenic climate change. This systematic review explores extreme events and their effect on gender-based violence (GBV) experienced by women, girls, and sexual and gender minorities. We searched ten databases until February, 2022. Grey literature was searched using the websites of key organisations working on GBV and Google. Quantitative studies were described narratively, whereas qualitative studies underwent thematic analysis. We identified 26 381 manuscripts. 41 studies were included exploring several types of extreme events (ie, storms, floods, droughts, heatwaves, and wildfires) and GBV (eg, sexual violence and harassment, physical violence, witch killing, early or forced marriage, and emotional violence). Studies were predominantly cross-sectional. Although most qualitative studies were of reasonable quality, most quantitative studies were of poor quality. Only one study included sexual and gender minorities. Most studies showed an increase in one or several GBV forms during or after extreme events, often related to economic instability, food insecurity, mental stress, disrupted infrastructure, increased exposure to men, tradition, and exacerbated gender inequality. These findings could have important implications for sexual-transformative and gender-transformative interventions, policies, and implementation. High-quality evidence from large, ethnographically diverse cohorts is essential to explore the effects and driving factors of GBV during and after extreme events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".